AI Threat Alert indexes 3,082+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 841–860 of 3,082 papers
Hanbo Huang, Xuan Gong, Yiran Zhang +2 more
Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to...
Jinhua Wang, Biswa Sengupta
Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We...
Ricardo Bessa, Rui Claro, João Trindade +1 more
The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI...
Dzenan Hamzic, Florian Skopik, Max Landauer +2 more
Cyber threat intelligence (CTI) analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented...
Yiran Ling, Wenxuan Li, Siying Dong +5 more
Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show...
Yihao Zhang, Kai Wang, Jiangrong Wu +7 more
Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security...
Zhixiang Lu, Jionglong Su
Multimodal Large Language Models (MLLMs) in healthcare suffer from severe confirmation bias, often hallucinating visual details to support initial,...
Navid Azimi, Aditya Prakash, Yao Wang +1 more
Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications....
Shuhao Zhang, Yuli Chen, Jiale Han +2 more
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text....
Xiaomeng Hu, Yinger Zhang, Fei Huang +7 more
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor...
Maria Camporese, Fabio Massacci, Yuanjun Gong
[Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because...
Yuanbo Xie, Yingjie Zhang, Yulin Li +5 more
Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability:...
Vu Tuan Truong, Long Bao Le
Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior...
Jordi Cabot
There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software...
Yuchen Chen, Yuan Xiao, Chunrong Fang +2 more
The proliferation of large language models for code (CodeLMs) and open-source contributions has heightened concerns over unauthorized use of source...
Xuwei Ding, Skylar Zhai, Linxin Song +6 more
Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to...
Wenbo Hu, Xin Chen, Yan Gao-Tian +3 more
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal...
Runpeng Geng, Chenlong Yin, Yanting Wang +2 more
Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the...
Kevin Lira, Baldoino Fonseca, Davy Baía +2 more
Large Language Models (LLMs) have been a promising way for automated vulnerability detection. However, most prior studies have explored the use of...
Hanzhi Liu, Chaofan Shou, Hongbo Wen +3 more
Large language model (LLM) agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers....
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 3,082+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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